The promise of integrated care for mental illness prevention and treatment

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The impact of mental illness on individuals, families, the health system and even the economy is broad and significant. In this, the latest post in my mental health series, I’d like to talk about what can be done to help. Prevention and early detection are just parts of an integrated approach to care. A common approach to integrated care, when it comes to mental health, is combining mental health and primary care information and services. As I mentioned in my previous post, a person’s mental health has a tremendous effect on their physical well-being, and vice versa.

Cherokee Health Systems in Tennessee has integrated mental health and primary care services for over 25 years, resulting in enhanced patient satisfaction, enhanced quality of care, reductions of inpatient admissions, reduced ER visits, and increased primary care visits[1]. Studies in acute care have yielded positive results as well. Eight psychiatric hospitals in Florida reduced preventable readmissions from 17.7% to 10% by collaborating and coordinating hospital care and post-discharge care[2].

There are other approaches to integrated care taking off around the country. Medicare/Medicaid (at both the federal and state levels), private payers and many healthcare systems have looked to advance integrated care through delivery models such as Accountable Care Organizations, Managed Care Plans and Patient Centered Medical Homes. In addition, there are many efforts to prevent and treat serious and long term mental illness, such as:

  • Medicaid’s mandated EPSDT screening for early signs of mental illness
  • Federally supported demonstration waivers (which may include provisions for mental health enhancements/demonstrations)
  • Expansion of covered mental health services through private payers
  • Affordable Care Act mandated mental health quality reporting
  • The Mental Health Parity and Addiction Equity Act
  • State/regional/community health and human service programs
  • The efforts of various non-profit organizations

However, many of these initiatives to integrate care and implement successful prevention/early treatment depend on the valuable insight which exists in clinical EMR systems, administrative claims payment systems and many other government/non-government data systems. Integrating, enriching, analyzing and visualizing this ocean of data can help improve mental and physical health care, and ultimately provide better consumer and system outcomes. Broader adoption of advanced data management, analytics and data visualization technology will provide novel and more targeted, insightful approaches to looking at data. Data exists from a variety of areas such as:

  • Medicaid/Medicare Systems
  • Commercial Claim Data (potentially All Payer Claims Databases including Medicaid/Medicare)
  • Health Information Exchanges
  • Ambulatory/Acute/Post-Acute EHR Systems
  • Child/Youth/Family/Elderly/Disabled/Social Service systems
  • Education Systems
  • Welfare Management Systems
  • Juvenile Justice/Criminal Justice Systems
  • Social platforms (Facebook/Twitter/ etc.)

Linking the above data sources can provide a single, 360 degree view of all the health and social services provided to a patient and assist in the co-ordination/integration of care efforts. In doing so, the collective analytics can be operationalized in a number of ways to support the prevention/treatment of those with diagnosed/undiagnosed mental illness. Potential areas of analytic support include:

  • Enhancing coordination of care efforts that allow service providers and all stakeholders’ rapid access to a single source of data to analyze and coordinate treatment options in a collaborative environment
  • Risk adjustment of mental illness contributing factors through the mining of historical data and assigning individual services/behaviors/treatments different levels of risk based on positive or negative outcomes. Using an integrated data approach, risk factors need not be limited to one set of data, but instead can identify risk factors which are clinical and/or socio-economic in nature
  • Assisting prevention efforts by using the above risk adjusted data to create analytic models of clinical/social services/behaviors/treatments/other contributing variables which historically yield the most desirable/undesirable outcomes. Then, target prevention campaigns to identify the factors which could possibly yield negative outcomes.
  • Enhancing prevention/quality of care by predicting statistical likelihood of future outcomes and performing what-if scenario analysis to determine how specific clinical/social services/behaviors/treatments/other causal variables may affect future outcomes
  • Improving quality of care by analyzing the effectiveness of various treatment plans in order to replicate positive outcomes
  • Determining which service providers are providing the most/least desirable outcomes and understanding the causal factors
  • Increasing understanding of variables outside of the traditional health data realm that affect mental illness
  • Visualizing all of the data (separately or unified) in a single point of view environment
  • Attributing providers to episodes of care to better measure cost and quality

With these possibilities before us, what can we do to make them a more common reality? In the next section I’ll lay out why whatever investment it takes is worth it, and might even save us money.

[1] Agency for Healthcare Research and Quality, Research Activities No. 377, “Experts call for integrating mental health into primary care” (January 2012)

[2] American Hospital Association, TrendWatch, “Bringing Behavioral Health into the Care Continuum: Opportunities to Improve Quality, Costs and Outcomes” (January 2012)

 

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About Author

Jeremy Racine

Healthcare Strategy Consultant

Jeremy has immersed himself in big data and analytics for 14 years, including a comprehensive focus on health care and ways the public and private sectors can deploy technology to improve population health and reduce costs. He knows that analytics is critical to driving health care modernization, and understands the complex implications of the Affordable Care Act, new care and payment models and Medicaid changes. As a member of the SAS State & Local Government practice, Jeremy works with the public sector to help them understand how analytics can empower modernization and enhancements across the healthcare continuum.

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